Data quality is a shared responsibility and not just a job for the IT department.
This is according to Mark Kohlmeyer, chief architect of iOCO Qlik, who says business teams must own their data, and align data strategy with business objectives as well as taking an active role in governance.
“Many organisations lack accountability for data quality. This generally translates into ‘everyone is responsible’ which usually comes down to the fact that ‘nobody is responsible’. Clear ownership of the data quality processes is essential,” says Kohlmeyer.
He says that, in terms of data health and governance within the context of the evolving data landscape in Africa, increasingly businesses want data-driven outcomes, but face challenges in areas like data connectivity, trust, transformation, and analytics. “While data is more complex than ever, its value lies in enabling better decision-making and reducing risks like customer churn.
“Matters such as completeness, accuracy, timeliness, and accessibility – play a major role in data quality. Poor data quality can have a profound impact on an organisation’s reputation and bottom line making it all the more important to understand it is a shared responsibility between IT and business. There are outlined frameworks and processes that organisations can use to improve and maintain data quality,” he adds.
According to Kohlmeyer the following key components are necessary if businesses are to extract value from data in a world with intense demand for data-driven business outcomes:
- Connecting and moving data.
- Ensuring data trust.
- Transforming data.
- Storing it in business-ready formats.
- Analysing and predicting to extract value.
“Modern systems involve challenges such as new regulatory requirements (for example PoPIA), multiple data locations (cloud, on-premise), types (structured and unstructured) and business trust in data quality is an essential element for any data and AI project,” he notes.
Kohlmeyer cites EOH – one of South Africa’s largest technology services companies – and its data modernisation journey as an example of a company integrating Qlik and Talend as a tool to enhance data quality and governance.
“Through this partnership, EOH implemented a robust data governance framework, streamlined processes, and increased data accuracy, ultimately positioning the company to leverage reliable data for better decision-making.
“EOH acquired multiple business units, many with their own systems, leading to fragmented data architecture and inefficiencies. Some of the challenges included multiple ERP systems, inefficient processes, and a lack of master data governance.”
EOH partnered with iOCO to implement a data modernisation project, choosing Qlik and Talend as primary tools. “Talend was selected for its capabilities in data validation, enrichment, and workflow automation. The team focused on finance data as the first domain, cleansing and consolidating over 40 000 customer records down to around 14 000, showing the importance of continuous data improvement,” he confirms.
Kevin van der Merwe, iOCO Qlik business unit executive, says the long-term benefits for EOH include data modernisation which has led to improved data trust, enhanced operational efficiencies, and streamlined processes. “All of this combines to position EOH to leverage data more effectively for decision-making and compliance.”
Van der Merwe emphasises that, while tools enable, they don’t solve everything. “Technologies such as Qlik and Talend can automate and enhance data governance, but the responsibility still lies with business and people to ensure data accuracy and trust,” says van der Merwe.
Kohlmeyer concludes ultimately there are three key takeaways regarding data quality for all businesses to take note of:
- Data quality should be a collaborative effort between IT and business. Business owns the process and must align data strategy with its strategic goals.
- Cognisance of the impact of poor data quality: Poor data quality can severely affect both an organisation’s reputation and bottom line, as illustrated through real-world examples.
- Frameworks for data improvement: Organisations need to implement data improvement plans and governance frameworks, ensuring that data is consistently monitored and improved over time.
Written by Mark Kohlmeyer, Chief Architect of iOCO Qlik
Originally featured here